Association between Physical Activity, Sedentary Time, and Technology Use in Adults with Autism Spectrum Disorder

Background: Limited research indicates that adults with autism spectrum disorder (ASD) do not meet physical activity (PA) guidelines and are highly sedentary. Inadequate PA is a leading preventable cause of death worldwide, but little is known about PA, sedentary time (ST), and factors influencing these variables in adults with ASD. Technology is used as an educational and social intervention for people with ASD, yet is also associated with low PA and high ST in the neurotypical population. It is important to determine if technology use is also associated with low PA and high ST in adults with ASD to better understand these health variables in this unique population.

Objectives: (1) Assess PA and ST levels, and technology use in a large sample of adults with ASD and (2) examine factors that influence PA and ST, particularly technology, in adults with ASD.

Methods: A self-report, online survey addressing autism symptoms (Autism Spectrum Quotient-10; AQ10), PA and ST (International Physical Activity Questionnaire), and technology use was developed, assessed for content validity, and pretested with 6 adults with ASD. Participants were recruited via ASD support groups in social media and direct contact with ASD advocacy organizations in the U.S. and other English-speaking countries. A 15-minute minimum survey completion time and AQ10 score ⩾6 were used to verify valid responses and meeting the inclusion criteria. Stepwise forward multiple regressions were generated to explain the variation in PA and ST, with demographic variables, time spent in technology use, and AQ10 score as predictor variables. No multicollinearity was observed in the models. Analyses were performed using SPSS and significance level was set at p < 0.05.

Results: Of the 802 survey responses received, 229 were included in the analyses. Adults with ASD engaged in moderate to vigorous PA (MVPA) x̅=128.3±114.8 min/week. Median sedentary time was similar for weekdays (470 min/day, IQR = 300) and weekends (420 min/day, IQR= 240). Participants spent x̅=590.2±331.7 min/day using technology devices for Internet surfing (x̅=145.8±126.2 min/day), entertainment (x̅ = 106.8±93.4 min/day), and social media (x̅=80±69.1 min/day). Moderate positive correlations were found between technology use and weekdays (r = 0.416, p < 0.001) and weekends (r = 0.417, p < 0.001) ST. AQ10 score was the strongest negative predictor of total PA time in 3 regression models (β = -0.360, p < 0.001; β = -0.323, p < 0.001; β = -0.258, p = 0.001). Technology use time strongly predicted ST in both weekdays (β = 0.399, p < 0.001) and weekends (β = 0.422, p < 0.001).

Conclusions: Adults with ASD do not acquire the recommended 150 min/week of MVPA, spend excessive time using technology and are highly sedentary. Additionally, more autistic symptoms and more time spent using technology lead to more ST. These data indicate that PA and ST interventions are needed to meet the unique health needs of adults with ASD, particularly those with more symptoms. It is recommended that technology use to be leveraged as an intervention tool to address these health variables in adults with ASD.